{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beyond 1\n",
    "\n",
    "Rather than starting with data from Chicago, start with an empty data frame, and use a `for` loop to load data from all three cities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>maxtemp</th>\n",
       "      <th>mintemp</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-12-11 00:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11 03:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11 06:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11 09:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-11 12:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>Chicago</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     maxtemp  mintemp     city\n",
       "date_time                                     \n",
       "2018-12-11 00:00:00        1       -2  Chicago\n",
       "2018-12-11 03:00:00        1       -2  Chicago\n",
       "2018-12-11 06:00:00        1       -2  Chicago\n",
       "2018-12-11 09:00:00        1       -2  Chicago\n",
       "2018-12-11 12:00:00        1       -2  Chicago"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame()\n",
    "\n",
    "all_dfs = []\n",
    "\n",
    "for city_stem in ['chicago,il', 'los+angeles,ca', 'boston,ma']:\n",
    "    new_df = pd.read_csv(f'../data/{city_stem}.csv', \n",
    "                 usecols=[0, 1,2],\n",
    "                 header=0,\n",
    "                 names=['date_time','maxtemp', 'mintemp'],\n",
    "                parse_dates=['date_time'],\n",
    "                index_col=['date_time'])\n",
    "    new_df['city'] = city_stem.split(',')[0].replace('+', ' ').title()\n",
    "    all_dfs.append(new_df)\n",
    "\n",
    "df = pd.concat(all_dfs)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beyond 2\n",
    "\n",
    "For each city, calculate the mean and median for `mintemp` and `maxtemp`. Are they the same (or even close)? If they're different, in which direction were they pulled?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">mintemp</th>\n",
       "      <th colspan=\"2\" halign=\"left\">maxtemp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Boston</th>\n",
       "      <td>-3.142857</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2.868132</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chicago</th>\n",
       "      <td>-5.076923</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-0.736264</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <td>10.637363</td>\n",
       "      <td>11.0</td>\n",
       "      <td>17.054945</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               mintemp           maxtemp       \n",
       "                  mean median       mean median\n",
       "city                                           \n",
       "Boston       -3.142857   -3.0   2.868132    2.0\n",
       "Chicago      -5.076923   -4.0  -0.736264    0.0\n",
       "Los Angeles  10.637363   11.0  17.054945   16.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('city')[['mintemp', 'maxtemp']].agg(['mean', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beyond 3\n",
    "\n",
    "Create a line plot showing the minimum temperatures in each city. The x axis should show dates, the y axis should show temperatures, and each line should represent a different city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "Boston         AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Chicago        AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Los Angeles    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: mintemp, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('city')['mintemp'].plot.line(legend=True)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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